CN111490905A - Method for automatically inspecting video image fault - Google Patents
Method for automatically inspecting video image fault Download PDFInfo
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- CN111490905A CN111490905A CN202010358950.7A CN202010358950A CN111490905A CN 111490905 A CN111490905 A CN 111490905A CN 202010358950 A CN202010358950 A CN 202010358950A CN 111490905 A CN111490905 A CN 111490905A
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- 238000000034 method Methods 0.000 title claims abstract description 19
- 238000001514 detection method Methods 0.000 claims abstract description 43
- 238000012423 maintenance Methods 0.000 claims abstract description 21
- 238000007689 inspection Methods 0.000 claims abstract description 19
- 238000013528 artificial neural network Methods 0.000 claims abstract description 10
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 claims description 3
- 230000008054 signal transmission Effects 0.000 claims description 3
- 238000004891 communication Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000008439 repair process Effects 0.000 description 2
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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- G—PHYSICS
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- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C1/00—Registering, indicating or recording the time of events or elapsed time, e.g. time-recorders for work people
- G07C1/20—Checking timed patrols, e.g. of watchman
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/04—Network management architectures or arrangements
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Abstract
The invention discloses a method for automatically inspecting video image faults, which comprises the following steps: s1: the method comprises the steps that a video detection device is arranged, and detection data of a terminal are collected in real time and sent to a background management server; s2: inputting the extracted detection data into a preset neural network fault judgment model, and outputting a detection analysis result and an early warning signal; s3: and the background server pushes the detection and analysis result to the management terminal in the form of an intelligent report, controls the management terminal to send out early warning information, and performs terminal operation, maintenance and detection by management and maintenance personnel according to the indication information and the early warning information of the intelligent report. According to the invention, the video detection device is arranged and integrated with the background management server, and the preset neural network fault judgment model is used for detecting the video signals and the video source equipment, so that automatic remote inspection is realized and the inspection efficiency is improved.
Description
Technical Field
The invention relates to the technical field of video inspection, in particular to a method for automatically inspecting video image faults.
Background
In recent years, with the continuous promotion and deepening of the construction of the electronic government affairs in China, the electronic government affairs bring great convenience to the public social life and improve the working efficiency of government departments, and meanwhile, the electronic government affairs are important measures for the nation to implement government function transformation and improve government management, public service and emergency capacity, and are favorable for driving the development of the whole national economy and social informatization. The electronic government affair market scale is a national administrative management form in which affairs such as daily office, information collection and release, public management and the like of government agencies are carried out in a digital and networked environment under the technical support of modern computers, network communication and the like. The system comprises various contents, such as government office automation, information co-construction and sharing among government departments, government real-time information release, remote video conferences among all levels of governments, national online government information inquiry, electronic opinion investigation, social and economic statistics and the like.
At present, more manual on-site maintenance and maintenance for maintenance of the electronic government affair interaction terminal are realized, remote intelligent inspection can not be realized for video detection of the terminal, and inspection efficiency is low.
Disclosure of Invention
The invention provides a method for automatically inspecting video faults, aiming at overcoming the defects that the inspection efficiency of a terminal video is low and remote inspection cannot be realized in the prior art.
The primary objective of the present invention is to solve the above technical problems, and the technical solution of the present invention is as follows:
a method for automatic video image fault inspection comprises the following steps:
s1: the method comprises the steps that a video detection device is arranged, and detection data of a terminal are collected in real time and sent to a background management server;
s2: inputting the extracted detection data into a preset neural network fault judgment model, and outputting a detection analysis result and an early warning signal;
s3: and the background server pushes the detection and analysis result to the management terminal in the form of an intelligent report, controls the management terminal to send out early warning information, and performs terminal operation, maintenance and detection by management and maintenance personnel according to the indication information and the early warning information of the intelligent report.
In this embodiment, the detection data in step S1 includes: a terminal ID number, video signal data, and video source device status data.
In this scheme, the video signal data fault types include:
if the video signal collected by the terminal is lost, judging that the collection terminal is in fault or lost;
if the automatic focusing evaluation value and the brightness value of the collected video signal are abnormal, judging that a detection area of the terminal is shielded;
and if the image pixels of the previous frame and the current frame of the video signal collected by the terminal are different, judging that the video of the collecting terminal is fuzzy.
In this scheme, the management terminal includes: mobile terminal, PC terminal.
In the scheme, in step S3, the operation and maintenance personnel take maintenance and repair measures according to the intelligent report and the early warning information, wherein the maintenance measures include: and remotely restarting and adjusting the code stream of the video signal transmission of the terminal.
In the scheme, the video detection device is arranged inside the terminal and binds the ID number of the terminal. .
In this scheme, step S3 further includes storing the detection analysis result in a fault database according to the fault type, the fault frequency, and the fault scenario, and the background server optimizes the neural network fault determination model using data in the fault database.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
according to the invention, the video detection device is arranged and integrated with the background management server, and the preset neural network fault judgment model is used for detecting the video signals and the video source equipment, so that automatic remote inspection is realized, and the inspection efficiency is improved.
Drawings
Fig. 1 is a flow chart of a method for automatic video image fault inspection according to the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
Example 1
As shown in fig. 1, a method for automatic inspection of video image failure includes the following steps:
s1: the method comprises the steps that a video detection device is arranged, and detection data of a terminal are collected in real time and sent to a background management server; in a specific embodiment, the video detection devices may be respectively disposed inside terminals, the video detection terminals are in communication connection with the background management server, and data collected by the video detection devices are all sent to the background management server.
S2: inputting the extracted detection data into a preset neural network fault judgment model, and outputting a detection analysis result and an early warning signal;
s3: and the background management server pushes the detection and analysis result to the management terminal in the form of an intelligent report, controls the management terminal to send out early warning information, and performs terminal operation, maintenance and detection by management and maintenance personnel according to the indication information and the early warning information of the intelligent report.
In this embodiment, the detection data in step S1 includes: a terminal ID number, video signal data, and video source device status data.
The confirmation and the positioning of the terminal can be realized by detecting the ID number of the terminal in the data, and the background management server can inquire the information of the terminal such as the area where the terminal is located, the operation time of the terminal and the past fault record in a correlation mode according to the ID number of the terminal.
In this scheme, the video signal data fault types include:
if the video signal collected by the terminal is lost, judging that the collection terminal is in fault or lost;
if the automatic focusing evaluation value and the brightness value of the collected video signal are abnormal, judging that a detection area of the terminal is shielded;
and if the image pixels of the previous frame and the current frame of the video signal collected by the terminal are different, judging that the video of the collecting terminal is fuzzy.
In this scheme, the management terminal includes: mobile terminal, PC terminal.
In the scheme, in step S3, the operation and maintenance personnel take maintenance and repair measures according to the intelligent report and the early warning information, wherein the maintenance measures include: and remotely restarting and adjusting the code stream of the video signal transmission of the terminal.
In the scheme, the video detection device is arranged inside the terminal and binds the ID number of the terminal. .
In this scheme, step S3 further includes storing the detection analysis result in a fault database according to the fault type, the fault frequency, and the fault scenario, and the background server optimizes the neural network fault determination model using data in the fault database. The neural network fault judgment model is further optimized by using the fault database, so that the accuracy of detection and analysis of the model can be improved, and the inspection efficiency is improved.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (7)
1. A method for automatically inspecting video image faults is characterized by comprising the following steps:
s1: the method comprises the steps that a video detection device is arranged, and detection data of a terminal are collected in real time and sent to a background management server;
s2: inputting the extracted detection data into a preset neural network fault judgment model, and outputting a detection analysis result and an early warning signal;
s3: and the background server pushes the detection and analysis result to the management terminal in the form of an intelligent report, controls the management terminal to send out early warning information, and performs terminal operation, maintenance and detection by management and maintenance personnel according to the indication information and the early warning information of the intelligent report.
2. The method for automatic video image fault inspection according to claim 1, wherein the detection data in step S1 includes: a terminal ID number, video signal data, and video source device status data.
3. The method for video image fault automatic inspection according to claim 2, wherein the fault type of the video signal data comprises:
if the video signal collected by the terminal is lost, judging that the collection terminal is in fault or lost;
if the automatic focusing evaluation value and the brightness value of the collected video signal are abnormal, judging that a detection area of the terminal is shielded;
and if the image pixels of the previous frame and the current frame of the video signal collected by the terminal are different, judging that the video of the collecting terminal is fuzzy.
4. The method for automatic routing inspection of video image faults according to claim 1, wherein the management terminal comprises: mobile terminal, PC terminal.
5. The method for automatic routing inspection of video image faults according to claim 1, wherein in step S3, the operation and maintenance personnel takes maintenance operation and maintenance measures according to the intelligent report and the early warning information, wherein the maintenance measures include: and remotely restarting and adjusting the code stream of the video signal transmission of the terminal.
6. The method for automatic routing inspection of video image faults as claimed in claim 1, wherein the video detection device is arranged inside the terminal and binds an ID number of the terminal.
7. The method for video image fault automatic inspection according to claim 1, wherein the step S3 further includes storing the detection analysis results to a fault database according to fault type, fault frequency and fault scenario, and the background server optimizes the neural network fault judgment model using data in the fault database.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202010358950.7A CN111490905A (en) | 2020-04-29 | 2020-04-29 | Method for automatically inspecting video image fault |
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| CN202010358950.7A CN111490905A (en) | 2020-04-29 | 2020-04-29 | Method for automatically inspecting video image fault |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112182068A (en) * | 2020-09-30 | 2021-01-05 | 重庆市海普软件产业有限公司 | Remote fault judgment system and method based on Internet of things technology |
| CN113411204A (en) * | 2021-05-17 | 2021-09-17 | 吴志伟 | Method and device for detecting faults of telecommunication access network facilities and computer storage medium |
| CN114070828A (en) * | 2022-01-17 | 2022-02-18 | 中央广播电视总台 | Program stream fault detection method and device, computer equipment and readable storage medium |
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| CN107272637A (en) * | 2017-06-06 | 2017-10-20 | 武汉瑞科兴业科技有限公司 | A kind of video monitoring system fault self-checking self- recoverage control system and method |
| CN108337321A (en) * | 2018-03-14 | 2018-07-27 | 卡斯柯信号有限公司 | CBTC signalling arrangements cruising inspection system and method based on video intelligent identification |
| CN110751270A (en) * | 2019-10-23 | 2020-02-04 | 广东工业大学 | Unmanned aerial vehicle wire fault detection method, system and equipment |
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2020
- 2020-04-29 CN CN202010358950.7A patent/CN111490905A/en active Pending
Patent Citations (4)
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| CN106454331A (en) * | 2016-11-02 | 2017-02-22 | 北京弘恒科技有限公司 | A video signal quality detection system and method |
| CN107272637A (en) * | 2017-06-06 | 2017-10-20 | 武汉瑞科兴业科技有限公司 | A kind of video monitoring system fault self-checking self- recoverage control system and method |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112182068A (en) * | 2020-09-30 | 2021-01-05 | 重庆市海普软件产业有限公司 | Remote fault judgment system and method based on Internet of things technology |
| CN112182068B (en) * | 2020-09-30 | 2024-03-26 | 重庆市海普软件产业有限公司 | Remote fault judging system and method based on Internet of things technology |
| CN113411204A (en) * | 2021-05-17 | 2021-09-17 | 吴志伟 | Method and device for detecting faults of telecommunication access network facilities and computer storage medium |
| CN114070828A (en) * | 2022-01-17 | 2022-02-18 | 中央广播电视总台 | Program stream fault detection method and device, computer equipment and readable storage medium |
| CN114070828B (en) * | 2022-01-17 | 2022-05-17 | 中央广播电视总台 | Program stream fault detection method and device, computer equipment and readable storage medium |
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